import edu.stanford.nlp.tagger.maxent.MaxentTagger;


public class main {

	/**
	 * @param args
	 */
	public static void main(String[] args) {
//		// creates a StanfordCoreNLP object, with POS tagging, lemmatization, NER, parsing, and coreference resolution 
//	    Properties props = new Properties();
//	    props.put("annotators", "tokenize, ssplit, pos, lemma, ner, parse, dcoref");
//	    StanfordCoreNLP pipeline = new StanfordCoreNLP(props);
//	    
//	    // read some text in the text variable
//	    String text = "The efficiency of personal video suggestions generated by recommender systems is highly dependent on the quality of the obtained user feedback. This feedback has to reflect the personal interest in the content of the viewed video, to obtain accurate results. However, user feedback might undesirably be influenced by additional aspects such as the loading speed or the quality of the video. To date, this issue has received very little research attention. Therefore, this study investigates the direct influence of audio-visual quality parameters on explicit user feedback for the first time to our knowledge via a mobile, Living Lab experiment. This paper proposes a feedback model which takes the Quality of Service (QoS) parameters of the mobile network into account. This model can be used as an additional feedback filter for video recommendation systems that could help to eliminate the influences of QoS on explicit user feedback."; // Add your text here!
//	    
//	    // create an empty Annotation just with the given text
//	    Annotation document = new Annotation(text);
//	    
//	    // run all Annotators on this text
//	    pipeline.annotate(document);
//	    
//	    // these are all the sentences in this document
//	    // a CoreMap is essentially a Map that uses class objects as keys and has values with custom types
//	    List<CoreMap> sentences = document.get(SentencesAnnotation.class);
//	    
//	    for(CoreMap sentence: sentences) {
//	      // traversing the words in the current sentence
//	      // a CoreLabel is a CoreMap with additional token-specific methods
//	      for (CoreLabel token: sentence.get(TokensAnnotation.class)) {
//	        // this is the text of the token
//	        String word = token.get(TextAnnotation.class);
//	        // this is the POS tag of the token
//	        String pos = token.get(PartOfSpeechAnnotation.class);
//	        // this is the NER label of the token
//	        String ne = token.get(NamedEntityTagAnnotation.class);       
//	      }
//
//	      // this is the parse tree of the current sentence
//	      Tree tree = sentence.get(TreeAnnotation.class);
//
//	      // this is the Stanford dependency graph of the current sentence
//	      SemanticGraph dependencies = sentence.get(CollapsedCCProcessedDependenciesAnnotation.class);
//	    }
//
//	    // This is the coreference link graph
//	    // Each chain stores a set of mentions that link to each other,
//	    // along with a method for getting the most representative mention
//	    // Both sentence and token offsets start at 1!
//	    Map<Integer, CorefChain> graph = 
//	      document.get(CorefChainAnnotation.class);
		
	     // Initialize the tagger
        MaxentTagger tagger = new MaxentTagger("edu/stanford/nlp/models/pos-tagger/english-left3words/english-left3words-distsim.tagger");
 
        // The sample string
        String sample = "This document contains (some) information about the models included in this release and that may be downloaded for the POS tagger website at";
 
        // The tagged string
        String tagged = tagger.tagString(sample);
 
        // Output the result
        System.out.println(tagged);

	}

}
